Display sensitivity maps for EEG and MEG sensorsΒΆ

Sensitivity maps can be produced from forward operators that indicate how well different sensor types will be able to detect neural currents from different regions of the brain.

To get started with forward modeling see ref:tut_forward.

# Author: Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)

import mne
from mne.datasets import sample
import matplotlib.pyplot as plt


data_path = sample.data_path()

raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'

subjects_dir = data_path + '/subjects'

# Read the forward solutions with surface orientation
fwd = mne.read_forward_solution(fwd_fname, surf_ori=True)
leadfield = fwd['sol']['data']
print("Leadfield size : %d x %d" % leadfield.shape)


Reading forward solution from /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    2 source spaces read
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    MEG and EEG forward solutions combined
    Source spaces transformed to the forward solution coordinate frame
    Converting to surface-based source orientations...
    Average patch normals will be employed in the rotation to the local surface coordinates....
Leadfield size : 366 x 22494

Compute sensitivity maps

grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed')
mag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='fixed')
eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')


204 out of 366 channels remain after picking
    102 out of 366 channels remain after picking
    60 out of 366 channels remain after picking
Adding average EEG reference projection.

Show gain matrix a.k.a. leadfield matrix with sensitivity map

picks_meg = mne.pick_types(fwd['info'], meg=True, eeg=False)
picks_eeg = mne.pick_types(fwd['info'], meg=False, eeg=True)

fig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
fig.suptitle('Lead field matrix (500 dipoles only)', fontsize=14)
for ax, picks, ch_type in zip(axes, [picks_meg, picks_eeg], ['meg', 'eeg']):
    im = ax.imshow(leadfield[picks, :500], origin='lower', aspect='auto',
    plt.colorbar(im, ax=ax, cmap='RdBu_r')

plt.hist([grad_map.data.ravel(), mag_map.data.ravel(), eeg_map.data.ravel()],
         bins=20, label=['Gradiometers', 'Magnetometers', 'EEG'],
         color=['c', 'b', 'k'])
plt.title('Normal orientation sensitivity')

grad_map.plot(time_label='Gradiometer sensitivity', subjects_dir=subjects_dir,
              clim=dict(lims=[0, 50, 100]))
  • ../../_images/sphx_glr_plot_forward_sensitivity_maps_001.png
  • ../../_images/sphx_glr_plot_forward_sensitivity_maps_002.png
  • ../../_images/sphx_glr_plot_forward_sensitivity_maps_003.png


Updating smoothing matrix, be patient..
Smoothing matrix creation, step 1
Smoothing matrix creation, step 2
Smoothing matrix creation, step 3
Smoothing matrix creation, step 4
Smoothing matrix creation, step 5
Smoothing matrix creation, step 6
Smoothing matrix creation, step 7
Smoothing matrix creation, step 8
Smoothing matrix creation, step 9
Smoothing matrix creation, step 10
colormap: fmin=2.11e-02 fmid=3.22e-01 fmax=1.00e+00 transparent=1

Total running time of the script: ( 0 minutes 17.123 seconds)

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